Comparing Pandas and PySpark 04 Jul 2023

Data processing and analysis are crucial components of any data-driven project. Python provides several powerful libraries for data manipulation, such as Pandas and PySpark. In this post, we will explore the differences between these two popular frameworks by examining an example code snippet in both Pandas and PySpark. We will focus on the similarities and divergences in their syntax, functionality, and performance.

Consider this situation and the different ways to solve them using Pandas or Pyspark:

Video Streaming Data Exploration

You work for a video streaming platform and are given a DataFrame containing information about videos available on the platform. The DataFrame named input_df that has the following schema:

+--------------+-----------+
| Column Name  | Data Type |
+--------------+-----------+
| video_id     | Integer   |
| title        | String    |
| genre        | String    |
| release_year | Integer   |
| duration     | Integer   |
| view_count   | Integer   |
+--------------+-----------+

Your task is to write a function etl that takes in the input DataFrame and returns a DataFrame containing only the videos with more than 1,000,000 views and released in the last 5 years. The output DataFrame should have the same schema as the input DataFrame.

The initial code snippet utilizes the Pandas library to perform data extraction, transformation, and loading (ETL) operations. Let’s break down the Pandas code:

import pandas as pd
import numpy as np
import datetime
import json
import math
import re

def etl(input_df):
    current_year = datetime.datetime.now().year
    filtered_df = input_df[(input_df['view_count'] > 1000000) & (input_df['release_year'] >= current_year - 5)]
    return filtered_df

In this code, the Pandas library is imported along with other required modules. The etl function takes an input DataFrame and applies a filter to extract rows where the ‘view_count’ is greater than one million and the ‘release_year’ is within the last five years. The resulting filtered DataFrame is then returned.

Now let’s explore the equivalent code in PySpark, which is a distributed data processing framework built on Apache Spark:

from pyspark.sql import SparkSession
from pyspark.sql import functions as F
from pyspark.sql import Window as W
import pyspark
import datetime
import json

spark = SparkSession.builder.appName('run-pyspark-code').getOrCreate()

def etl(input_df):
    current_year = datetime.datetime.now().year
    filtered_df = input_df.where((F.col("view_count") > 1000000) & (F.col("release_year") >= current_year - 5))
    return filtered_df

The PySpark code begins with the necessary imports, including SparkSession, functions from the pyspark.sql module, and other required libraries. A SparkSession is created to initiate the Spark application. The etl function in PySpark is analogous to the one in Pandas but utilizes PySpark’s DataFrame operations. Here, the where function applies the same filtering conditions as in the Pandas code.

Differences and Similarities

1. Syntax:
   • Pandas: Pandas code follows a more concise and Pandas-specific syntax, leveraging DataFrame and Series objects for data manipulation.
   • PySpark: PySpark code is more verbose and resembles SQL-like syntax due to its reliance on distributed data processing.
2. Performance:
   • Pandas: Pandas is primarily designed for single-machine processing, making it suitable for small to medium-sized datasets.
   • PySpark: PySpark is built for distributed computing, allowing it to handle large-scale datasets and parallelize operations across a cluster of machines.
3. Scalability:
   • Pandas: Pandas operates in-memory, limiting its scalability to the available memory of a single machine.
   • PySpark: PySpark’s distributed architecture enables it to scale horizontally across a cluster, processing massive datasets.
4. Ecosystem and Integration:
   • Pandas: Pandas has a rich ecosystem of libraries for various data analysis tasks, along with seamless integration with other Python libraries.
   • PySpark: PySpark benefits from the broader Spark ecosystem, which includes machine learning libraries (Spark MLlib), graph processing (GraphX), and real-time streaming (Spark Streaming).

Both Pandas and PySpark are powerful tools for data processing and analysis, but they differ significantly in terms of syntax, performance, scalability, and ecosystem. Pandas excels in single-machine environments, providing an intuitive and efficient interface for data manipulation. On the other hand, PySpark’s distributed architecture enables it to handle big data workloads and leverage the Spark ecosystem for more extensive data processing tasks. The choice between Pandas and PySpark depends on the specific requirements of the project, the size of the dataset, and the available computational resources.